In platforms like n8n, you literally drag and drop tools onto your AI agent β like giving your butler a tray of abilities. Connect a weather API node, and boom β your AI can check weather. Add a Google Sheets node, now it can update spreadsheets. Each connection is a new skill. π» API Tool Calling When coding, you define available tools in your API request:
The AI decides WHEN to use tools (just like a butler knows when to go to the kitchen), calls them, gets results, and continues the conversation with real data. Enter MCP: Your Tool Organisation System Here's where it gets interesting. As you add more tools, things can get messy fast. Imagine your butler having 50 different abilities but no organised way to manage them. That's where Model Context Protocol (MCP) comes in. MCP is like a tool cabinet for your AI. Instead of scattered tools everywhere, you have:
We recently covered how to build your own internal MCP server in n8n β turning your custom business tools into a professional toolkit your AI agents can use instantly. βοΈ Knowledge Shots 1οΈβ£ The "Loop" that changes everything: AI agents can check β decide β act β verify β adjust. This loop is what makes them feel autonomous. They're not just responding β they're thinking and adapting. 2οΈβ£ Tools aren't just for data: Your AI can send emails, create calendar events, update databases, generate images, or even control smart home devices. If there's an API for it, your AI can probably use it. 3οΈβ£ Platform differences:
π§ Why This Matters Right Now AI is at a turning point: itβs no longer just suggesting ideas. It can take action. One prompt can now trigger dozens of automated tasks, moving AI from βneat toyβ to a practical assistant. But while tool calling and orchestration unlock real productivity, you still need humans in the loop. AI can handle the busywork, but judgment and oversight remain essential. That's the revolution happening right now. And you can build it today.
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